Inmar and I get into a meaty discussion about graphical models, including what they are and how they’re used, some of the challenges they present for both training and inference, and how and where they can be best applied. Then we jump into an in-depth look at the key ideas behind the Min-Max Propagation paper itself, including the relationship to the broader domain of belief propagation and ideas like affinity propagation, and how all these can be applied to a use case example like the makespan problem. This was a really fun conversation! Enjoy!

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